Artificial Neural Network prediction of forming limit diagram for directionally-rolled, size scaled copper strips

Abstract

Estimating the forming limit diagram (FLD) is tedious and cost-intensive. Methods driven by data and artificial intelligence are used to determine the relationship between scaled thickness and the forming rates of various cups drawn out of ETP copper sheets. Machine learning (ML) techniques have a good chance of predicting the FLD of copper alloys, and they are being used increasingly in sensitive electronic and structural applications. The current research aims to create ML-based artificial neural network (ANN) tools to model the relationship between scaled thickness and forming rates as a function of formability. The FLD is measured for copper strips of initial dimensions of 1500 mm long, 750 mm wide, and 6 mm thick, whose thickness was reduced successively by 50% in nine incremental steps. Thus, 3, 1.5, 0.75, 0.38, and 0.19 mm sheets were obtained and used to determine FLD through the Nakajima approach. An FEA model of the drawing was made in Altair Inspire Form, and the simulation results were used to train a two-step ML. A Bayesian regularization (BR) and Levenberg-Marquardt algorithm (LM) were used in the first step to predict strains’ maximum and minimum points. In the second step, the minor strains predicted in the first step are used as inputs. Using the same feature set, the BR and LM algorithms predict the major strain, showing a linear trend until the middle and then a nonlinear trend. The trained ML model was used to predict unknown intermediate values for estimating the over-learning and over-fitting problems here for 2 and 0.25 mm thick sheets and are validated experimentally. The variation between the FLDs of predicted and experimentally verified data falls between 2% and 5%. Such small changes in the FLD values show that the proposed ML model could be used to predict the FLDs of copper strips.

Numerical Optimization of Variable Blank Holder Force Trajectories in Stamping Process for Multi-Defect Reduction

“…Therefore, analytical and numerical methods for determining the FLC have been developed as listed in Table 2 . Researchers considered Punch stroke, oil pressure [ 30 ], forming rates [ 40 ], and chemical composition with temperature conditions [ 28 ] to train artificial intelligence models, while some other authors mainly considered material properties such as YS, UTS, EU, EL, etc., and supplemented them with simple engineering conditions such as R , n , t , etc., to predict FLC [ 29 , 41 , 42 ]. The current work utilized appropriate experimental data and advanced machine learning modeling to enhance predictability, enabling more efficient and cost-effective manufacturing practices by reducing the reliance on extensive physical testing.…”